


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class    __a						( __snake_case					,			__snake_case					,			__snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       Any			       =StableDiffusionInstructPixaPixPipeline
				lowerCamelCase     :       int			       =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
				lowerCamelCase     :       Tuple			       =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCamelCase     :       Optional[Any]			       =IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase     :       Optional[Any]			       =IMAGE_TO_IMAGE_IMAGE_PARAMS







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					torch.manual_seed(0   )
					lowerCAmelCase_      							=			UNetaDConditionModel(
					    block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=8      ,	out_channels=4      ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,	cross_attention_dim=32      ,	)
					lowerCAmelCase_      							=			PNDMScheduler(skip_prk_steps=UpperCAmelCase   )
					torch.manual_seed(0   )
					lowerCAmelCase_      							=			AutoencoderKL(
					    block_out_channels=[32, 64]      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,	latent_channels=4      ,	)
					torch.manual_seed(0   )
					lowerCAmelCase_      							=			CLIPTextConfig(
					    bos_token_id=0      ,	eos_token_id=2      ,	hidden_size=32      ,	intermediate_size=37      ,	layer_norm_eps=1E-0_5      ,	num_attention_heads=4      ,	num_hidden_layers=5      ,	pad_token_id=1      ,	vocab_size=1000      ,	)
					lowerCAmelCase_      							=			CLIPTextModel(UpperCAmelCase   )
					lowerCAmelCase_      							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

					lowerCAmelCase_      							=			{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					return components







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase=0   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(UpperCAmelCase   )   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			image.cpu().permute(0      ,	2      ,	3      ,	1   )[0]
					lowerCAmelCase_      							=			Image.fromarray(np.uinta(UpperCAmelCase   )   ).convert('''RGB'''   )
					if str(UpperCAmelCase   ).startswith('''mps'''   ):
						lowerCAmelCase_      							=			torch.manual_seed(UpperCAmelCase   )
					else:
						lowerCAmelCase_      							=			torch.Generator(device=UpperCAmelCase   ).manual_seed(UpperCAmelCase   )
					lowerCAmelCase_      							=			{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': image,
					    '''generator''': generator,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 6.0,
					    '''image_guidance_scale''': 1,
					    '''output_type''': '''numpy''',
					}
					return inputs







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase_      							=			self.get_dummy_components()
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe.to(UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			self.get_dummy_inputs(UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1]
					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase_      							=			np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase_      							=			self.get_dummy_components()
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe.to(UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			self.get_dummy_inputs(UpperCAmelCase   )
					lowerCAmelCase_      							=			'''french fries'''
					lowerCAmelCase_      							=			sd_pipe(**UpperCAmelCase      ,	negative_prompt=UpperCAmelCase   )
					lowerCAmelCase_      							=			output.images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase_      							=			np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase_      							=			self.get_dummy_components()
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe.to(UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			self.get_dummy_inputs(UpperCAmelCase   )
					lowerCAmelCase_      							=			[inputs['''prompt''']] * 2

					lowerCAmelCase_      							=			np.array(inputs['''image''']   ).astype(np.floataa   ) / 2_5_5.0
					lowerCAmelCase_      							=			torch.from_numpy(UpperCAmelCase   ).unsqueeze(0   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			image / 2 + 0.5
					lowerCAmelCase_      							=			image.permute(0      ,	3      ,	1      ,	2   )
					lowerCAmelCase_      							=			image.repeat(2      ,	1      ,	1      ,	1   )

					lowerCAmelCase_      							=			sd_pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[-1, -3:, -3:, -1]

					assert image.shape == (2, 32, 32, 3)
					lowerCAmelCase_      							=			np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowerCAmelCase_      							=			self.get_dummy_components()
					lowerCAmelCase_      							=			EulerAncestralDiscreteScheduler(
					    beta_start=0.0_0_0_8_5      ,	beta_end=0.0_1_2      ,	beta_schedule='''scaled_linear'''   )
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe.to(UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			self.get_dummy_inputs(UpperCAmelCase   )
					lowerCAmelCase_      							=			sd_pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1]

					lowerCAmelCase_      							=			[round(UpperCAmelCase      ,	4   ) for x in image_slice.flatten().tolist()]
					print(''','''.join([str(UpperCAmelCase   ) for x in slice]   )   )

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase_      							=			np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_dummy_components()
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline(**UpperCAmelCase   )
					lowerCAmelCase_      							=			VaeImageProcessor(do_resize=UpperCAmelCase      ,	do_normalize=UpperCAmelCase   )
					lowerCAmelCase_      							=			pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )

					lowerCAmelCase_      							=			pipe(**self.get_dummy_inputs_by_type(UpperCAmelCase      ,	input_image_type='''pt'''   )   )[0]

					lowerCAmelCase_      							=			components['''vae''']
					lowerCAmelCase_      							=			self.get_dummy_inputs_by_type(UpperCAmelCase      ,	input_image_type='''pt'''   )

					for image_param in self.image_latents_params:
						if image_param in inputs.keys():
							lowerCAmelCase_      							=			vae.encode(inputs[image_param]   ).latent_dist.mode()

					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   )[0]

					lowerCAmelCase_      							=			np.abs(out - out_latents_inputs   ).max()
					self.assertLess(UpperCAmelCase      ,	1E-4      ,	'''passing latents as image input generate different result from passing image'''   )







@slow
@require_torch_gpu
class    __a						( unittest.TestCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase=0   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			torch.manual_seed(UpperCAmelCase   )
					lowerCAmelCase_      							=			load_image(
					    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''   )
					lowerCAmelCase_      							=			{
					    '''prompt''': '''turn him into a cyborg''',
					    '''image''': image,
					    '''generator''': generator,
					    '''num_inference_steps''': 3,
					    '''guidance_scale''': 7.5,
					    '''image_guidance_scale''': 1.0,
					    '''output_type''': '''numpy''',
					}
					return inputs







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    '''timbrooks/instruct-pix2pix'''      ,	safety_checker=UpperCAmelCase   )
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			self.get_inputs()
					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase_      							=			np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]   )

					assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    '''timbrooks/instruct-pix2pix'''      ,	safety_checker=UpperCAmelCase   )
					lowerCAmelCase_      							=			LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			self.get_inputs()
					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase_      							=			np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]   )

					assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    '''timbrooks/instruct-pix2pix'''      ,	safety_checker=UpperCAmelCase   )
					lowerCAmelCase_      							=			DDIMScheduler.from_config(pipe.scheduler.config   )
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			self.get_inputs()
					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   ).images
					lowerCAmelCase_      							=			image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					lowerCAmelCase_      							=			np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]   )

					assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			0

					def callback_fn(UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ) -> None:
						lowerCAmelCase_      							=			True
						nonlocal number_of_steps
						number_of_steps += 1
						if step == 1:
							lowerCAmelCase_      							=			latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCAmelCase_      							=			latents[0, -3:, -3:, -1]
							lowerCAmelCase_      							=			np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]   )

							assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
						elif step == 2:
							lowerCAmelCase_      							=			latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCAmelCase_      							=			latents[0, -3:, -3:, -1]
							lowerCAmelCase_      							=			np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]   )

							assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

					lowerCAmelCase_      							=			False

					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    '''timbrooks/instruct-pix2pix'''      ,	safety_checker=UpperCAmelCase      ,	torch_dtype=torch.floataa   )
					lowerCAmelCase_      							=			pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			self.get_inputs()
					pipe(**UpperCAmelCase      ,	callback=UpperCAmelCase      ,	callback_steps=1   )
					assert callback_fn.has_been_called
					assert number_of_steps == 3







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    '''timbrooks/instruct-pix2pix'''      ,	safety_checker=UpperCAmelCase      ,	torch_dtype=torch.floataa   )
					lowerCAmelCase_      							=			pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing(1   )
					pipe.enable_sequential_cpu_offload()

					lowerCAmelCase_      							=			self.get_inputs()
					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   )

					lowerCAmelCase_      							=			torch.cuda.max_memory_allocated()
					# make sure that less than 2.2 GB is allocated
					assert mem_bytes < 2.2 * 10**9







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.get_inputs()
					# resize to resolution that is divisible by 8 but not 16 or 32
					lowerCAmelCase_      							=			inputs['''image'''].resize((504, 504)   )

					lowerCAmelCase_      							=			'''timbrooks/instruct-pix2pix'''
					lowerCAmelCase_      							=			StableDiffusionInstructPixaPixPipeline.from_pretrained(
					    UpperCAmelCase      ,	safety_checker=UpperCAmelCase      ,	)
					pipe.to(UpperCAmelCase   )
					pipe.set_progress_bar_config(disable=UpperCAmelCase   )
					pipe.enable_attention_slicing()

					lowerCAmelCase_      							=			pipe(**UpperCAmelCase   )
					lowerCAmelCase_      							=			output.images[0]

					lowerCAmelCase_      							=			image[255:258, 383:386, -1]

					assert image.shape == (504, 504, 3)
					lowerCAmelCase_      							=			np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5E-3



"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class    __a						( __snake_case							):
				lowerCamelCase     :       List[Any]			       ='mobilenet_v2'







				def __init__( self      ,	UpperCAmelCase=3      ,	UpperCAmelCase=224      ,	UpperCAmelCase=1.0      ,	UpperCAmelCase=8      ,	UpperCAmelCase=8      ,	UpperCAmelCase=6      ,	UpperCAmelCase=32      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase="relu6"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.8      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=0.0_0_1      ,	UpperCAmelCase=255      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(**UpperCAmelCase   )

					if depth_multiplier <= 0:
						raise ValueError('''depth_multiplier must be greater than zero.'''   )

					lowerCAmelCase_      							=			num_channels
					lowerCAmelCase_      							=			image_size
					lowerCAmelCase_      							=			depth_multiplier
					lowerCAmelCase_      							=			depth_divisible_by
					lowerCAmelCase_      							=			min_depth
					lowerCAmelCase_      							=			expand_ratio
					lowerCAmelCase_      							=			output_stride
					lowerCAmelCase_      							=			first_layer_is_expansion
					lowerCAmelCase_      							=			finegrained_output
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			tf_padding
					lowerCAmelCase_      							=			classifier_dropout_prob
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			layer_norm_eps
					lowerCAmelCase_      							=			semantic_loss_ignore_index







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =version.parse('1.11'							)







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.task == "image-classification":
						return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
						return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return 1E-4